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Mastra

TypeScript agent framework for building AI agents, workflows, memory, tool calling, and evaluation-backed applications.

by Mastra·added 2026-04-27·
CLI
HarnessCLI
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Open the source and read safety notes before installing.

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Canonical URL
https://heyclau.de/entry/tools/mastra
Source URLs
https://mastra.ai/docs, https://github.com/mastra-ai/mastra, https://mastra.ai
Brand
Mastra
Brand domain
mastra.ai
Brand asset source
brandfetch
Author
Mastra
Claim status
unclaimed
Last verified
2026-04-27

Schema details

Install type
copy
Troubleshooting
No
Source repository stats
Scope
Source repo
Tool listing metadata
Pricing
open-source
Disclosure
heyclaude_pick
Application category
DeveloperApplication
Operating system
macOS, Windows, Linux, Web
Full copyable content
## Editorial notes

Mastra is useful for JavaScript and TypeScript teams building agent products with workflow, memory, and tool abstractions.

## Disclosure

Editorial listing. No paid placement or affiliate link is used.

About this resource

Editorial notes

Mastra is useful for JavaScript and TypeScript teams building agent products with workflow, memory, and tool abstractions.

Disclosure

Editorial listing. No paid placement or affiliate link is used.

#agent-framework#typescript

Source citations

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How it compares

Mastra side by side with 3 alternatives on trust, install, platform support, and disclosed safety notes — all from reviewed registry metadata.

Field
Mastra logoMastra

TypeScript agent framework for building AI agents, workflows, memory, tool calling, and evaluation-backed applications.

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OpenAI logoOpenAI Agents JavaScript SDK

Official JavaScript and TypeScript framework for building multi-agent workflows with agents, tools, handoffs, guardrails, sessions, tracing, realtime voice agents, MCP tools, hosted tools, and sandbox agents.

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VoltAgent logoVoltAgent

Open-source TypeScript agent engineering framework and platform for building AI agents with tools, memory, workflows, RAG, guardrails, evals, MCP, voice, and VoltOps observability.

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AG2 Agent Framework logoAG2 Agent Framework

Open-source Python AgentOS and multi-agent framework, evolved from AutoGen, for building conversable agents, group chats, swarms, human-in-the-loop workflows, tool use, RAG, code execution, and provider-backed agent systems.

Open dossier
Trust
Install riskReview firstReview firstReview firstReview first
Notes Safety · Privacy · Safety ✓ Privacy ✓ Safety ✓ Privacy ✓ Safety ✓ Privacy ✓
BrandMastra logoMastraOpenAI logoOpenAIVoltAgent logoVoltAgentAG2 Agent Framework logoAG2 Agent Framework
Categorytoolstoolstoolstools
Sourcefirst-partysource-backedsource-backedsource-backed
AuthorMastraOpenAIVoltAgentAG2
Added2026-04-272026-06-182026-06-182026-06-18
Platforms
CLI
CLI
CLI
CLI
Source repo————
Safety notes— missing✓Agents can call function tools, hosted tools, MCP tools, realtime tools, and sandbox agents; treat every tool as an API endpoint with explicit authorization, input validation, rate limits, and side-effect controls. Sandbox agents can inspect files, run commands, apply patches, and carry workspace state across longer tasks; restrict workspace scope and require human approval before destructive or high-impact actions. Cloudflare Workers support is described upstream as experimental; review runtime compatibility, secrets, outbound network access, logging, request limits, and `nodejs_compat` behavior before production use. Guardrails help validate inputs and outputs, but they do not replace permission checks, least-privilege credentials, audit logs, or human review for risky operations. Handoffs and agents-as-tools can delegate work across agents; document which agent owns each tool, decision, retry, rollback, and escalation path.✓VoltAgent agents can call application tools, MCP tools, model providers, workflow steps, memory adapters, RAG retrievers, and voice providers, so each integration needs explicit permission and review boundaries. Typed tools and Zod schemas help define contracts, but they do not prove that an agent action is correct, reversible, policy-compliant, or safe for production. Workflows can run application code, call APIs, suspend, resume, branch, run steps in parallel, and execute agent steps; review long-running and human approval flows before using them with real customer or infrastructure actions. MCP support can expose filesystem, browser, database, cloud, or internal-service tools from external servers; use narrow server allowlists and audit tool descriptions before attaching them to agents. Guardrails and evals are useful release controls, but production agents still need logs, rollback paths, rate limits, budget limits, and human review for high-impact actions.✓AG2 agents can converse, call tools, execute code, use retrieval systems, run browser workflows, and coordinate group chats; require explicit permissions and approval gates for high-impact actions. The upstream install docs and examples commonly involve provider credentials; keep API keys, config files, notebooks, and `.env` files out of commits and support tickets. Code execution, Docker, Jupyter, browser-use, and RAG extras can touch local files, network services, notebooks, databases, and external websites; scope them tightly before granting agent access. Multi-agent conversations can continue through nested chats, swarms, group chats, and custom reply handlers; define termination, escalation, retry, and human takeover behavior. Track the release roadmap before upgrading because deprecations and the v1.0 transition can change which APIs should be used for new work.
Privacy notes— missing✓Prompts, instructions, tool arguments, tool outputs, session history, traces, realtime audio events, sandbox files, logs, provider responses, and errors may contain user or workspace data. Do not expose secrets, tokens, private file paths, customer records, credentials, internal identifiers, raw exceptions, or voice transcripts through traces, logs, prompts, tool schemas, or examples. When using MCP servers, hosted tools, session stores, worker logs, observability systems, or deployment platforms, review each service's retention, access control, and third-party data handling separately. If sandbox agents operate on repositories or user files, define which files can be mounted, modified, committed, uploaded, logged, or returned to the model.✓Prompts, instructions, tool arguments, tool results, workflow state, memory records, retrieved documents, voice inputs or outputs, traces, eval data, and logs may be sent to model providers, storage systems, MCP servers, or VoltOps depending on configuration. Do not commit model API keys, MCP credentials, database URLs, webhook secrets, customer data, or prompt logs in the generated project. Durable memory and RAG integrations can retain user messages, document chunks, embeddings, and metadata; define retention and deletion rules before production use. When using VoltOps Console or self-hosted observability, review what traces, prompts, tool calls, metrics, and eval outputs are collected and who can access them.✓Prompts, messages, tool arguments, tool outputs, code snippets, notebook state, retrieved documents, vector-store contents, provider responses, traces, and execution logs may contain sensitive user or workspace data. Do not expose secrets, API keys, private file paths, customer records, internal documents, database rows, or raw exceptions through agent messages, logs, notebooks, screenshots, or public examples. Provider extras and retrieval integrations can route data through OpenAI, Anthropic, Google, AWS, local model servers, databases, vector stores, browser automation, or other third-party services. If AG2 is used for code execution or browser automation, define which files, domains, credentials, downloads, screenshots, and logs can be read or retained.
Prerequisites— none listed
  • Node.js 22 or later, Deno, Bun, or an explicitly reviewed Cloudflare Workers runtime with `nodejs_compat` enabled.
  • OpenAI API credentials or another configured model provider supported through the SDK's provider-agnostic routes.
  • A reviewed tool boundary for function tools, hosted tools, MCP tools, handoffs, sandbox agents, and any external systems the agent can call.
  • A TypeScript schema strategy for `zod`, tool inputs, tool outputs, guardrails, and runtime validation.
  • Node.js 20 or newer and a package manager compatible with the generated VoltAgent project.
  • Model provider credentials for the selected provider, such as OpenAI, Anthropic, Google, or another supported route.
  • A TypeScript application boundary for exposing agent endpoints, workflows, tools, memory, and observability.
  • Database, vector store, memory adapter, or knowledge-base plan before enabling durable memory or RAG.
  • Python 3.10 or newer and a Python environment managed with pip, uv, or another package manager.
  • Model provider credentials for the selected provider extra, such as OpenAI, Anthropic, Gemini, Bedrock, Mistral, Ollama, Groq, xAI, or another supported route.
  • A secrets strategy for provider keys, AG2 config files, `.env` files, notebooks, and example `OAI_CONFIG_LIST`-style credentials.
  • A reviewed execution boundary for code execution, Docker, Jupyter, browser-use, RAG, retrieval, database, and external tool extras.
Install—
npm install @openai/agents zod
npm create voltagent-app@latest
pip install 'ag2[openai]'
Config————
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